Name
Abstract | ||
|---|---|---|
This paper addresses improving the computational efficiency of the 3D point cloud reconstruction pipeline using uncalibrated image sequences. In existing pipelines, the bundle adjustment is carried out globally, which is quite time consuming since the computational complexity keeps growing as the number of image frames is increased. Furthermore, a searching and sorting algorithm needs to be used in order to store feature points and 3D locations. In order to reduce the computational complexity of the 3D point cloud reconstruction pipeline, a local refinement approach is introduced in this paper. The results obtained indicate that the introduced local refinement improves the computational efficiency as compared to the global bundle adjustment. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2000483 | REAL-TIME IMAGE AND VIDEO PROCESSING 2013 |
Keywords | Field | DocType |
3D point cloud reconstruction, bundle adjustment, computationally efficient bundle adjustment | Computer vision,Pipeline transport,Bundle adjustment,Artificial intelligence,Point cloud,Sorting algorithm,Computational complexity theory,Physics | Conference |
Volume | ISSN | Citations |
8656 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-Hsiang Chang | 1 | 103 | 10.91 |
| Nasser D. Kehtarnavaz | 2 | 534 | 66.02 |
| k raghuram | 3 | 0 | 0.34 |
| Robert Bogdan Staszewski | 4 | 536 | 93.76 |